How to Shrink Big Data To Fit Your Marketing Strategy

Technology has greatly influenced marketing in the past few years — so much so that marketers increasingly feel more like statisticians.

Generic marketing is no longer enough to establish a brand or to sell a product or service. Instead, success depends on a deep understanding of personalization tactics, customer intent and ways to measure marketing performance.

These tactics rely on establishing correlation among data trends. And one of the things that are crucial to that understanding is R programming.

R Programming is a language protocol that can help marketers make better use of big data and advanced analytics.

What It's All About

R is an open source programming language. It was released in 1995 as a development of a previous programming language, S.

It is specifically designed to explore data sets with statistical calculations and permit vectors, matrices, arrays and other data structures to be graphed for data visualization.

Analysts and data scientists can conduct linear and nonlinear modeling, time-series analysis and other statistical models.

To use, R is downloaded onto a laptop, on which R code is written as commands. Commands are entered into documents called a script.

These scripts are usually treated like a dedicated text file just like that for JavaScript or HTML.

Users type their code in a terminal console. For those who prefer a more graphic-friendly format to code to organize commands and scripts more easily, graphic user interfaces (GUIs) are available.

Data scientists who use R regularly use RStudio, while Deducer is another useful GUI.

Solving Problems

The interest in R grew among developers thanks to the introduction of packages – plug-in subprograms that extend R functions and capability.

Thus developers found numerous ways to solve technical programs and expand its use for other business professionals.

There are open source communities for R collaborations, contributing package ideas and solution to usage concerns.

The tech community was at the forefront of using R analysis. Facebook has used R programming to analyze its streams and refine its algorithm.

R has been also used for novelty purposes. A New York Times timeline comparing the influence of Michael Jackson albums against releases from other iconic music acts was developed using R.

Interest in R expanded to the mainstream business community as managers demanded more advanced analysis.

Marketers Take Notice

Because marketing is increasingly data driven by right time messaging, R is appealing. It offers the right structure for developing big data analysis on semi-structured data that comes as offshoots of social media and right-time campaigns — product reviews, image shares and likes, for example.

This discovery of purpose has lead to significant investment. Microsoft made news when it purchased Revolution Analytics, an agency dedicated to R programming services.

The application of R provides a perfect counterbalance to limitations in other solutions.

Using Excel to analyze quantities of information is challenging once the column and row limitations are reached. While generous, a 16,000-column limit and a 1 million-row limit may still not be enough for techniques outside of simple data validation.

At the other end of the spectrum are commercially available tools such as SAS to cull business intelligence. But approving a budget for a commercial tool can overcommit company resources.

A viable online community supports R programming, so the cost barrier of commitment is lower than that of many other solutions.

And it is much more adept and efficient in data preparation than Excel. R accommodates scripting, which allows for automation and faster analysis on sizeable datasets than Excel, for example.

Not Perfect

One downside is a processor performance concern that can impact calculations in R.

Analyzed elements are held in virtual memory, which in turn is limited by the RAM on the computer in which R is installed.

But packages have been introduced to ease big data calculation and to work with other popular database protocols such as Hadoop with less memory demand.

The adoption of R has been quick because it is available as an open source program.

The adoption has been so rapid that some business experts suggest R programming would replace enterprise statistics solutions, such as SPSS, simply due to its open source — i.e. free — structure.

But more than likely most professionals using R will be content with it as a complementary solution for exploring datasets. That sentiment remains consistent across industries and does not appear to be changing anytime soon.

R ultimately is becoming the heart of statistics modeling, programming and data visualization.

R can connect the hard numbers and supporting statistics to customer behavior and actionable responses. Such numbers are the ones that marketers will want to reflect upon the most.

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